This invention pertains to methods and apparatus for geophysical exploration. In particular, this invention pertains to methods and apparatus for creating velocity models for Pre-Stack Depth Migration (“PSDM”) via joint inversion (“JI”) of seismic, gravity (where gravity may include any type of scalar and/or vectorial gravity measurements and derived quantities such as: gravity field measurements, gradient measurements, Bouguer anomaly, etc.), and electromagnetic data (e.g., magnetotelluric (“MT”) and/or controlled-source electromagnetic (“CSEM”), where Controlled-Source Electromagnetic may include any geophysical exploration method based on electromagnetic induction in the earth, measured and/or computed in frequency or time domains).
Effective depth imaging through migration requires a reliable estimate of the seismic velocity model (i.e., an area or volumetric description of the speed of seismic waves like the compressional body wave velocity, commonly known as the P-wave velocity). Indeed, an incorrect seismic velocity model can cause severe lateral and vertical mispositioning of reflectors in depth other than avoiding the reconstruction of existing reflecting horizons. This problem severely impacts the exploration of hydrocarbons by increasing the risk of drilling dry wells or by misidentifying oil and gas-bearing structures.
The task of deriving a reliable P-wave velocity model is non-trivial, especially if the seismic data has poor Signal-to-Noise ratio, if there is little available a-priori information about subsurface seismic velocities, and if the subsurface geology has a complex laterally-varying structure. Problematic seismic imaging conditions are typically encountered in thrust-belt hydrocarbon prospects, but also for sub-basalt and sub-salt prospects (both land and marine). In such cases, the integration of multiple geophysical parameters can successfully reconstruct the seismic velocity distribution in depth with higher degrees of reliability than using the seismic method alone, thus reducing the exploration risks.
The derivation of a reliable velocity model can be performed through various approaches, including “model-driven” and “data-driven” methods. Model-driven methods transform a geological section directly into a velocity model to be used for PSDM. The convergence of the initial velocity estimate to the final velocity model is obtained in a trial-and-error approach consisting of manually changing the distribution of velocity in the model, performing a new PSDM and controlling the post-migration image gathers together with the geologic reliability. These methods may not always provide seismic velocity models that agree with the measured geophysical data (i.e., arrival times of seismic waves, observed gravity anomalies or calculated resistivity functions from electromagnetic measurements), and explore only a limited sub-group of models.
Data-driven methods, following a more rigorous approach (e.g., minimization of a cost function), always yield a model that fits the measured data, but the final velocity structure may not agree with geological considerations. Systematic and random errors in the input inversion data, non-uniqueness of the solution and sensitivity of the data to the model parameters (e.g., first-break tomography is more sensitive to high-velocity zones than to low-velocity ones, electromagnetic methods are more sensitive to conductive zones than to resistive ones) provide in many cases a difficult solution of the problem.
The integration of different sources of information (geophysical data, including seismic and non-seismic, a-priori information and interpretational constraints) reduces the non-uniqueness of the solution and provides improved seismic resolution in complex geology conditions. Previously known data integration techniques have been developed by deriving a model in one of the domains (generally seismic), transforming the data via empirical functions into another geophysical domain (e.g., density or resistivity) and then performing modeling or inversions in the corresponding non-seismic domain. In some cases, the resulting models could be transformed back into the seismic velocity domain to be used to improve the seismic imaging results.
Although such previously known data integration techniques are valuable in theory, they have several problems in practice. A primary problem consists of defining reliable functions relating seismic velocity to density or resistivity for transforming parameters between different geophysical domains. Another problem is that, although the target is the integration of data, the actual implementation of the described workflow gives greater weight to the seismic-derived model than the non-seismic methods. Thus, the non-seismic methods are confined to work around an initial seismic model, with little chance of substantially modifying it (especially in a linearized inversion approach). This inexact formulation of the integration problem is the main reason why the integration of different-nature geophysical data has been so far a matter of “art” related to the ability and experience of the geophysicists or interpreter, rather than related to any analytical and quantitative approaches.
It would be desirable to provide improved methods and apparatus for generating seismic imaging velocity models by integrating seismic, gravity, and electromagnetic (e.g., MT and/or CSEM) data.